Towards Automated Performance Optimization of BPMN Business Processes

TL;DR

This paper presents a methodology to convert BPMN models into annotated directed acyclic graphs to enable automated performance optimization, aiming to improve workflow efficiency and resilience.

Contribution

It introduces a novel mapping approach from BPMN to optimization-friendly graphs, facilitating automated performance improvements in business process models.

Findings

Mapping enables automated optimization algorithms.

Potential for significant performance improvements.

Provides concrete examples and discusses research challenges.

Abstract

Business Process Model and Notation (BPMN) provides a standard for the design of business processes. It focuses on bridging the gap between the analysis and the technical perspectives, and aims to deliver process automation. The aim of this technical report is to complement this effort by transferring knowledge from the related field of data-centric workflows aiming to provide automated performance optimization of the business process execution. Automated optimization lifts a burden from BPMN designers and increases workflow flexibility and resilience. As a key step towards this goal, the contribution of this work is to provide a methodology to map BPMNv2.0 models to annotated directed acyclic graphs, which emphasize the volume of the tokens exchanged and are amenable to existing automated optimization algorithms. In addition, concrete examples of mappings are given, while the optimization opportunities that are opened are explained, thus providing insights into the potential performance benefits and we discuss technical research issues.